{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "067337fd-5ed0-4217-818d-4891c54c78ef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-03-21T02:25:08.773586Z",
     "iopub.status.busy": "2022-03-21T02:25:08.773177Z",
     "iopub.status.idle": "2022-03-21T02:25:09.277967Z",
     "shell.execute_reply": "2022-03-21T02:25:09.277106Z",
     "shell.execute_reply.started": "2022-03-21T02:25:08.773557Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2064: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x[:, None]\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/axes/_base.py:248: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/axes/_base.py:250: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "Unknown property labe",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_181/728644712.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1800\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2040\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m80000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mchina\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'red'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabe\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gdp'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0max2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxes2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0max2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"美国\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfontproperties\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmyfont\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1865\u001b[0m                         \u001b[0;34m\"the Matplotlib list!)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1866\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1867\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1868\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1869\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1526\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_alias_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1527\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1528\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1529\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1530\u001b[0m             \u001b[0mlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_grab_next_args\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    404\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 406\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mseg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    407\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mseg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs)\u001b[0m\n\u001b[1;32m    394\u001b[0m                                   \"with non-matching shapes is deprecated.\")\n\u001b[1;32m    395\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mxrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mncx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mncy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 396\u001b[0;31m             \u001b[0mseg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mncx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mncy\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    397\u001b[0m             \u001b[0mret\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    398\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_makeline\u001b[0;34m(self, x, y, kw, kwargs)\u001b[0m\n\u001b[1;32m    298\u001b[0m         \u001b[0mdefault_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getdefaults\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setdefaults\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdefault_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 300\u001b[0;31m         \u001b[0mseg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    301\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mseg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    302\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, xdata, ydata, linewidth, linestyle, color, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[1;32m    419\u001b[0m         \u001b[0;31m# update kwargs before updating data to give the caller a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m         \u001b[0;31m# chance to init axes (and hence unit support)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 421\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    422\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpickradius\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickradius\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    423\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mind_offset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, props)\u001b[0m\n\u001b[1;32m    886\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    887\u001b[0m             ret = [_update_property(self, k, v)\n\u001b[0;32m--> 888\u001b[0;31m                    for k, v in props.items()]\n\u001b[0m\u001b[1;32m    889\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    890\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meventson\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    886\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    887\u001b[0m             ret = [_update_property(self, k, v)\n\u001b[0;32m--> 888\u001b[0;31m                    for k, v in props.items()]\n\u001b[0m\u001b[1;32m    889\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    890\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meventson\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstore\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36m_update_property\u001b[0;34m(self, k, v)\u001b[0m\n\u001b[1;32m    879\u001b[0m                 \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'set_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    880\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 881\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Unknown property %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    882\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    883\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: Unknown property labe"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.font_manager import FontProperties\n",
    "import pandas as pd\n",
    "data=pd.read_csv(\"line_animation.csv\")\n",
    "year=data['time']\n",
    "china=data['china']\n",
    "usa=data['usa']\n",
    "data.describe()\n",
    "myfont=FontProperties(fname=r\"/home/aistudio/external-libraries/微软雅黑.ttf\",size=12)\n",
    "fig2,axes2=plt.subplots(2,2)\n",
    "fig2.set_size_inches(5,5)\n",
    "fig2.suptitle(\"各国GDP\",fontproperties=myfont)\n",
    "ax1=axes2[0,0]\n",
    "ax1.set_title(\"中国\",fontproperties=myfont)\n",
    "ax1.set_xlim([1800,2040])\n",
    "ax1.set_ylim([0,80000])\n",
    "ax1.plot(year,china,color='red',labe='gdp')\n",
    "ax2=axes2[1,0]\n",
    "ax2.set_title(\"美国\",fontproperties=myfont)\n",
    "ax2.set_xlim([1800,2040])\n",
    "ax2.set_ylim([0,80000])\n",
    "ax2.plot(year,usa,color='blue')\n",
    "ax1.legend()\n",
    "ax2.legend(('the gdp of usa',),loc='right',fontsize=12)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "py35-paddle1.2.0"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
